Interpretable machine learning study of a collector based on combined twisted-tape and wavy-tape inserts

Nowadays, the efficiency of air collectors for solar thermal applications is still low, and many researchers tend to use machine learning to predict and model the performance of thermal systems, but most of the existing machine learning methods are uninterpretable, which poses a challenge for machine learning applications. In this paper, a new collector insert with enhanced heat transfer in the form of a combination of wave and helical twisted bands is firstly designed for performance test experiments using solar air collectors. Then, based on the test data, three mutually interpretable machine learning methods, PDP, ALE, and SHAP, are explored for predictive studies of collector performance. The results show that the average efficiency of the collector with inserted structure increases by 19.71 %, 12.25 %, and 17.53 % at inlet flow rates of 2.1 m/s, 3.3 m/s, and 4.5 m/s, respectively. The highest collector efficiency was achieved with a wave plate length of 360 mm, a helical twist ratio of 5.14, and an inlet flow rate of 4.5 m/s. Understanding how much the input affects the output through the interpretability of SHAP proves the value of interpretable machine learning, which is useful in guiding the modification of the collector structure.